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ILP Tuning for _foreach Vectorized Loads: Investigation & Results

Hypothesis

PyTorch's multi_tensor_apply kernel uses kILP = 4 for vectorized memory access. For 2-byte types (fp16, bf16), this means each vectorized load is 4 * 2 = 8 bytes (64-bit LDG.64). Modern GPUs support 128-bit loads (LDG.128). By increasing ILP to 8 for 16-bit types, each thread would load 16 bytes per instruction, potentially doubling memory throughput.

The change introduces effective_ilp():

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torch.compile Feature Completeness on Hopper & Blackwell: A Comprehensive Analysis

Author: Generated via Claude analysis of PyTorch codebase
Date: December 4, 2025
Focus: Understanding TMA and advanced features for H100/B100/B200 GPUs

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Executive Summary

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Table of Contents

• set -e, -u, -x, -o pipefail
• set -e
• set -x
• set -u
• set -o pipefail
• Setting IFS
• Original Reference

set -e, -u, -x, -o pipefail

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diff --git a/torchvision/datasets/mnist.py b/torchvision/datasets/mnist.py
index e87cd46e..bfd59914 100644
--- a/torchvision/datasets/mnist.py
+++ b/torchvision/datasets/mnist.py
@@ -131,6 +131,11 @@ class MNIST(VisionDataset):
     def class_to_idx(self) -> Dict[str, int]:
         return {_class: i for i, _class in enumerate(self.classes)}
 
+    def _check_raw_data_exists(self) -> bool:
+        return all([

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